This proposal is designed to address methodological issues believed to be important in epidemiologic studies of the role of occupation in the development of cardiovascular disease mortality. Since arteriosclerotic cardiovascular disease (ASCVD) accounts for approximately 35-40% of all deaths in the U.S.A., occupational exposures that are associated with relative risk (RR) of 1.3 for ASCVD are as important to detect and control from a public health point of view (measured in terms of excess deaths) as an exposure associated with a RR of 12 for cancer (e.g. bladder cancer) that accounts for less than 1% of all deaths in the U.S.A. Since the incidence of ASCVD is great, most large retrospective cohort studies have the power to detect a RR of 1.3. Thus the failure to detect important (RR greater than 1.3) occupational causes of ASCVD should not be a problem of power (sample size). Review of the epidemiological methods used to study mortalities suggests that few occupational causes of cardiovascular disease have been identified, in part, because of inappropriate comparisons of working populations with the general population. Recently, intracohort analyses that compare workers in different exposure categories within a single industry have been undertaken in an effort to develop a method which would properly estimate occupationally related risk of death from ASCVD. Unfortunately, as Gilbert has shown, the available statistical methods for intracohort analyses still tend to underestimate the effect of cumulative exposure. This underestimation results from utilizing date of death as the sole outcome variable. As a consequence, intracohort analyses may, also, fail to detect work-related disease when present. In this proposal a methodology is presented which, by utilizing date of termination of employment in addition to the date of death as an outcome variable, produces an unbiased estimate of effect of cumulative exposure. The accuracy of estimates obtained with the new method will be compared to those obtained with standard methods in computer simulations. Finally, mortality data from a large rubber worker cohort will be reanalyzed both with the new method and standard methods and the results compared. If the new method proves as useful as theoretical consideration would suggest, it should provide an improved means for detecting causes of chronic disabling diseases.